Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2013-001
July, 2013

Well-being effects of a major negative externality: The case of Fukushima

Katrin Rehdanza,bHeinz Welschc
Daiju Naritaa
Toshihiro Okubod【Abstract】
Following a major earthquake off the Pacific coast of Japan, a tsunami disabled thepower supply and cooling of three reactors in Fukushima, causing a major nuclear
accident on 11 March 2011. Based on a quasi-experimental difference-in-differences
approach we use panel data for 5,979 individuals interviewed in Japan before and after
the accident to analyze the effect of the accident on people’s subjective well-being. Our
main hypotheses are that this effect declines with distance to the place of the event but
also with distance to other nuclear power plants. To test these hypotheses, we use
Geographical Information Systems to merge the well-being data with information on
respondents’ distance to the Fukushima nuclear plant and on their proximity to nuclear
power stations in general. Our empirical results suggest the existence of significant
well-being effects of the combined event of the earthquake, tsunami and nuclear
accident that are proportional to proximity to the Fukushima site being equivalent to up
to 72 percent of annual household income. We find no evidence for increased
nation-wide worry about the presence of nuclear power plants near people’s place ofresidence.
aKiel Institute for the World Economy, Kiel, GermanybKiel University, Department of Economics, Kiel, GermanycUniversity of Oldenburg, Department of Economics, Oldenburg, GermanydKeio University, Faculty of Economics, Tokyo, Japan

Panel Data Research Center at Keio University
Keio University

Well-being effects of a major negative externality: The case of Fukushima
Katrin Rehdanza,b,*, Heinz Welschc , Daiju Naritaa and Toshihiro OkubodabcKiel Institute for the World Economy, Kiel, Germany

Kiel University, Department of Economics, Kiel, Germany

University of Oldenburg, Department of Economics, Oldenburg, GermanydKeio University, Faculty of Economics, Tokyo, Japan

Abstract
Following a major earthquake off the Pacific coast of Japan, a tsunami disabled the power
supply and cooling of three reactors in Fukushima, causing a major nuclear accident on 11
March 2011. Based on a quasi-experimental difference-in-differences approach we use panel
data for 5,979 individuals interviewed in Japan before and after the accident to analyze theeffect of the accident on people’s subjective well-being. Our main hypotheses are that thiseffect declines with distance to the place of the event but also with distance to other nuclear
power plants. To test these hypotheses, we use Geographical Information Systems to merge
the well-being data with information on respondents’ distance to the Fukushima nuclear plantand on their proximity to nuclear power stations in general. Our empirical results suggest theexistence of significant well-being effects of the combined event of the earthquake, tsunami
and nuclear accident that are proportional to proximity to the Fukushima site being equivalent
to up to 72 percent of annual household income. We find no evidence for increased nationwide worry about the presence of nuclear power plants near people’s place of residence.
Keywords: Fukushima, subjective well-being, nuclear disaster, difference-in-differences,willingness to pay
JEL codes: D62; Q51; Q54; I31*Corresponding author: katrin.rehdanz@ifw-kiel.de1

1. Introduction
Following a major earthquake off the Pacific coast of Japan, a tsunami disabled the power
supply and cooling of three reactors in Fukushima, causing a nuclear accident on 11 March
2011. The accident led to massive releases of radioactive materials and resulted in one of theworst nuclear disasters along with the Chernobyl disaster in 1986. The combined event of theearthquake, tsunami, and the nuclear accident caused nearly 16,000 deaths; over 1.2 million
destroyed or damaged buildings, and temporary evacuation of over 380,000 people from theirhome.1 It also disrupted water supply, power distribution, and train, highway and air transportsystems in a wide area of eastern Japan.
While some costs of natural disasters are direct and relatively easy to measure, such as the
reduction in national output, etc., others are indirect and much more difficult to quantify, suchas increased fear and anxiety and, more generally, mental distress. These intangible effects
need not be restricted to persons directly affected by the event. Rather, due to media coverage,
they may spill over to people at distant places.2 In particular, in the case of a major nuclear
accident, people may become worried about nuclear power in general, especially if living
close to nuclear facilities themselves.
Based on a quasi-experimental difference-in-differences (DD) approach we use panel data for
5,979 individuals interviewed in Japan before and after the accident to analyze, , the effect ofthe disasters on people’s subjective well-being (SWB) and compare it with the situation
before the accident.3 Our main hypotheses are that this effect declines with distance to theplace of the event but also with distance to nuclear power plants in general – due a decay ofphysical effects or of perceptions of the effect or due to a nation-wide worry about thepresence of nuclear power plants. To conduct our analysis, we use Geographical Information
Systems to merge the well-being data with information on respondents’ distance to the
Fukushima Dai-ichi nuclear power plant, on their proximity to nuclear power stations ingeneral and the spatial distribution of radioactive fallout after the accident. We further
consider that people might be differently affected by the nuclear accident and the tsunami byincluding information on which regions were affected by the tsunami.

1

http://www.reconstruction.go.jp/topics/000046.html, last accessed on April 5, 2013.
For instance, Kimball et al. (2006) found that after hurricane Katrina happiness of American citizens remotefrom the event dropped. Metcalfe et al. (2011) found that the terrorist attacks of 11 September 2001 caused
considerable mental distress on British citizens.
3
Please note that we use the terms life-satisfaction, happiness and well-being interchangeably throughout the
paper.
2

2

Our paper is the first study that uses a quasi-experimental DD framework to measure theeffect of the disaster on SWB and the implicit monetary value of the disaster. Regarding
nuclear accidents, Almond et al. (2009) have investigated the impacts of the Chernobyl
disaster on health and school outcomes, but not the effect on SWB and its monetaryequivalent. Berger (2010) found an increase in German citizens’ concern about theenvironment after Chernobyl, but no change in their life satisfaction. Other SWB studies ofsingle disasters are Kimball et al. (2006) and Metcalfe et al. (2011). While the former paper
analyzed the impact of hurricane Katrina on the happiness of Americans, the latter studied
mental distress in British citizens following the 9/11 attacks on the World Trade Center. In
addition, Luechinger and Raschky (2009) and Carroll et al. (2009), using a correlational
design, studied the relationship between floods and droughts, respectively, and SWB. To ourknowledge, no quasi-experimental SWB study of a nuclear accident that takes into accountthe spatial dimension has been conducted as yet.4
Our well-being data come from the Keio Household Panel Survey (KHPS), 2011 and 2012.
Our main measure of SWB is the answer to a happiness question in which individuals are
asked about their happiness in the previous year. Since the interviews in the KHPS are
conducted in January of the respective years, the answers from the 2011 survey refer to the“pre-Fukushima” period while those from the 2012 survey refer to the “post-Fukushima”
period. In addition to happiness with life in the previous year, people are asked abouthappiness with their entire life. In our econometric analysis we try different specifications
using one or the other as dependent variable testing if people’s assessment of the quality of
their life changed temporarily and/or entirely.
Our results suggest the following: after the disaster (1) SWB dropped in places affected by thetsunami but not elsewhere, (2) SWB is higher in places more distant from the Fukushima site,
respectively, (3) SWB is unaffected by the level of air dose rate suggesting an absence ofshort-term radiation-related impairments and (4) no nation-wide worry about nuclear power ofpeople living close to nuclear facilities can be detected. While our main indicator of SWB is“happiness with one’s life in the previous year”, an alternative measure is “happiness with
one’s whole life up to the present”. When we replace the former dependent variable with thelatter, we do not find any significant effect. The combined event of the earthquake, tsunami

4

Keio University published two books in Japanese containing a collection of papers based on KHPS data on
SWB and the accident (Seko et al. 2012 and 2013). But none of these papers discuss the Fukushima nuclear
accident taking into account the spatial dimension. For a general discussion of the use of SWB data ineconomics see subsection 2.1.

3and nuclear accident thus does not seem to have changed people’s assessment of the quality of
their entire life.
The paper is organized as follows. Section 2 provides a review of the literature on economics
and subjective well-being, and some information on the Fukushima nuclear accident. Section
3 presents the empirical approach and data. Section 4 reports the results. Section 5 provides a
discussion and conclusions.

2. Background
2.1 Literature Review: Economics and Subjective Well-Being
In economics, the interest in subjective well-being (often measured using “happiness” or “lifesatisfaction” questions) has increased rapidly over the last decade (for overviews see, e.g.,Frey and Stutzer, 2002; Dolan et al., 2008; van Praag and Ferrer-i-Carbonell, 2008;
MacKerron, 2012). The rationale for using data on subjective well-being in economic analysis
is that they are considered to be an empirical approximation to what Kahneman et al. (1997)
have labeled “experienced utility”.
Data on individuals’ subjective well-being are elicited in large-scale surveys in many
countries, such as the General Social Surveys in the U.S., the British Household Panel Survey,
the German Socio-Economic Panel or, more recently, the Keio Household Panel Survey inJapan. A precondition for using such data as a proxy for utility (or individual welfare) is thatthey satisfy appropriate quality requirements. In particular, they need to be at least ordinal incharacter and to satisfy conventional standards of consistency, validity and reliability.
Whether these conditions are being satisfied has been assessed in extensive validation
research (see, e.g., Frey and Stutzer 2002 for references). In these studies measures ofsubjective well-being (SWB) are generally found to have a sufficient degree of internalconsistency, validity, reliability, and a high degree of stability over time (Diener et al. 1999).
Different measures of SWB – especially measures of happiness and of life satisfaction correlate well with each other and, according to factor analyses, represent a single unitary
construct. Well-being responses are correlated with physical reactions that can be thought ofas describing true, internal happiness: People reporting to be happy tend to smile more andshow lower levels of stress responses (heart rate, blood pressure), and they are less likely tocommit suicide. Overall, measures of reported SWB can be viewed as valid and reliable
empirical approximations to individual utility.
Research on SWB has identified a number of personal, demographic and socio-economic
covariates that explain observed SWB. Important personal and demographic characteristics
4

which affect happiness are health, age, gender, marital status, the size and structure of thehousehold, the education level, and the degree of urbanization (Dolan et al. 2008).
Among the socio-economic determinants of SWB, important factors are personal income (orhousehold income) and the employment status. With respect to personal income, a robust
finding is that increasing personal income raises happiness (Clark et al. 2008). Regarding theemployment status, being unemployed has a strong negative association with SWB; this istrue even when controlling for income. Personal unemployment is the strongest individuallevel factor for low SWB (Frey and Stutzer 2002).
Important factors at the societal level are macroeconomic conditions (unemployment rate,inflation rate), institutional conditions (political freedom, democracy, the rule of law), publicbads (terrorism, civil war, corruption), and environmental amenities. The unemployment rate
and the inflation rate affect SWB negatively (Di Tella et al. 2001) whereas good institutional
quality yields greater SWB (Frey and Stutzer 2000). Public bads, such as terrorism, civil war,and corruption have sizeable negative effects on happiness (Frey et al. 2009, Welsch 2008a,
Welsch 2008b, respectively). With regard to terrorism, Metcalfe et al. (2011) have shown thatthe 9/11 attacks on the World Trade Center had sizeable and statistically significant effects on
well-being even in a place remote from the event, the U.K. Kimball et al. (2006) investigatedchanges in happiness of US adults after hurricane Katrina. Results of their descriptive analysis
point to a temporary effect of the event.
With regard to environmental (dis)amenities, the issues addressed so far relate to aconsiderable range of environmental problems and several forms of environment-related
extreme events. They comprise air pollution (Welsch 2002, 2006; Luechinger 2009;
MacKerron and Mourato 2009; Ferreira and Moro 2010; Levinson 2012), airport noise (vanPraag and Baarsma 2005), climate parameters (Rehdanz and Maddison 2005; Maddison andRehdanz 2011; Murray et al. 2013), flood events (Luechinger and Raschky 2009) and drought
events (Carroll et al. 2009). All of these studies found that SWB is positively related toenvironmental quality and negatively related to environmental extreme events.
Since well-being regressions involving non-market goods or extreme events include income
among the explanatory variables, they have been used to calculate the monetary equivalentsof those goods or events, that is, marginal rates of substitution or the compensating variation(see Welsch and Kühling 2009 for a discussion and review).
2.2 The Fukushima Nuclear Accident

5

The 2011 earthquake off the Pacific coast of Tohoku, which had magnitude 9.0 and was the
fourth largest earthquake in the world since 1990, occurred on March 11, 2011, off the coastof Miyagi prefecture, a prefecture in the northeastern region of Japan. Soon after theearthquake, the Fukushima Dai-ichi nuclear power plant, which is sited next to the Pacific
Ocean and 180 km off the epicenter, experienced failures in the cooling systems of thereactors due to a large tsunami, and four of its six reactors had massive releases of radioactive
materials after meltdowns and gas explosions. Those releases brought more than 500,000 TBq(terabecquerel) of highly toxic radioactive materials (iodine-131, cesium-134, and cesium137) into the atmosphere and the ocean (TEPCO, 2012), and the accident resulted in one of
the worst nuclear disasters along with the Chernobyl disaster in 1986.5
According to the Reconstruction Agency,6 the combined disaster of the earthquake, tsunami,
and the nuclear accident caused nearly 16,000 deaths, over 1.2 million destroyed or damagedbuildings, temporary evacuation of over 380,000 people from their home, most of whom wereresidents of Iwate, Miyagi, and Fukushima prefectures on the northeast coast of the Pacific
Ocean (see Figure 1). It also disrupted water supply, power distribution, and train, highway
and air transport systems in a wide area of eastern Japan. Reconstruction of infrastructures has
been partly hindered by radioactive contamination around the nuclear power plant, and as ofspring 2013, some key infrastructures, such as a major train line and a major highway (Joban
Line and Joban Expressway), have not been recovered yet.
After the nuclear accident, no deaths from radiation exposure have been reported, and longterm radioactivity-related health risks for the Fukushima residents are considered to be low(WHO, 2013). Still, radioactivity added a special dimension to the problem. 7 To reduceradiation exposure, all residents approximately within a 20 km radius of the Fukushima Daiichi power plant were forced to leave their home. 8 Although the surroundings of the
Fukushima plant had hardly been a population center or a popular touristic destination,
extended periods of evacuation placed severe stress on the evacuees. Due to contamination ofradioactive iodine, many local health authorities, including that of Tokyo, 220 km off the
Fukushima site, issued recommendations not to give tap water to infants, although most of
those warnings were lifted within a month after the disaster. Also, radioactive contaminationof farming products was widely detected in Fukushima and the neighboring prefectures.

5

The numbers for Chernobyl are about 10 times larger.
http://www.reconstruction.go.jp/topics/000046.html, last accessed on April 5, 2013.
7
Facts presented in the following are according to the Nuclear Emergency Response Headquarters (2011).
8
The number of evacuees from the evacuation zone amounted to approximately 113,000 (estimated by the
Cabinet Office in February 2012).
6

6

Although those products did not enter the market, such cases of contamination economicallyhurt farmers and also provoked public anxieties about food safety.
Before March 2011, nuclear power had provided about 30% of electricity in Japan, and all the
nuclear power plants in the country were gradually suspended after the Fukushima disaster tobe given a comprehensive safety test. Supply of electricity was particularly tightened in thesummer 2011, a peak season of electricity demand due to the use of air conditioning.
Rationing of electricity was placed on large industrial customers (> 500kW), and also thegovernment mounted a large-scale public campaign on conservation of electricity byindividual consumers.
Figure 1 displays the distribution and location of the 21 nuclear power stations in Japan,
highlighting Fukushima Dai-ichi. The power plants are distributed across the whole country
all located at the coast with some clustering.
Figure 1 about here
Social consequences of the disaster, especially those of the nuclear accident, are substantial
for the country: for example, there have already been some major changes in energy policy
following the event. The Fukushima disaster served as a major impetus to the introduction ofa feed-in-tariff scheme for renewable energy, which started in July 2012. The Cabinet alsodecided in September 2012 that it would not allow any new constructions of nuclear power
plants and would phase out all the existing plants by 2030.9

3. Methodology and Data
3.1 General Approach and Empirical Strategy
We use a quasi-experimental difference-in differences (DD) approach to measure the effect ofthe disaster on SWB, whilst controlling for a range of other factors. The DD design allows us
to isolate the effect attributable to the disaster from other contemporaneous variables (e.g.
macroeconomic changes), since the control group experiences some or all of thecontemporaneous influences that affect SWB in the treatment group without being affected by
the event.
9

Although the new government since December 2012 led by the Liberal Democratic Party repeatedly expresses
intentions to reconsider the decision (for example, Asahi Shimbun, January 31, 2013, “genpatsu zero, shushou
minaosu, shuin daihyou shitsumon”: in Japanese, “the zero nuclear power plants target should be reconsidered,said PM in a Diet interpellation”).

7The general model employed for this purpose is:

β

β

β

∗

1

where SWBijt is the stated subjective well-being of respondent i in location j at time t, Iit is
respondent i’s income, Hit represents other socioeconomic and demographic characteristics,
Gjt are region specific information. Ei denotes event variables that indicate whether or to what
extent an individual has been affected (treated) by the disaster, Yt is a dummy variable
representing the year of the interview. For reasons of data availability (see below), ouranalysis is restricted to two years, and we set Yt = 1 if the interview took place in the year
after the disaster, Yt = 0 otherwise. The symbol ε represents the error term.
The interaction term between the event (treatment) variable and the year of the interview tells
us how the disaster might have changed SWB of persons affected by the disaster. It should be
noted, however, that we are unable to check common trends in SWB before the event, a key
identifying assumption of DD, since we have SWB data only for one year before the event.
Within this general framework we estimate a set of different specifications which differ by theway treatment is captured. Among others this includes a measure that identifies which areas
were directly affected by the disaster and measures that take into account distance orproximity to the Fukushima site. To identify how severely a region was affected by thedisaster we collected information on the number of missing, injured or dead people as well as
the number of buildings completely or partly destroyed. This information is available at thelevel of the municipality.10 While this latter measure (equation 2 below) is more likely tocapture the impact of the tsunami caused by the earthquake the distance measures are meant
to capture the impact of the nuclear accident (equations 3 and 4 below).
To estimate the impact of the tsunami on SWB we use the following equation:

β

β

β

∗

10

Data was available from the National Research Institute for Earth Science and Disaster Prevention,
http://www.j-risq.bosai.go.jp/ndis, last accessed 20-05-2013)82

where the key variable of interest, Tj, represents a dummy variable denoting 1 if the individual
was living in a municipality being affected by the tsunami and 0 otherwise (Ei = Tj).11 The
parameter 4 represents the difference in SWB in the municipalities affected by the tsunami
with respect to unaffected municipalities, whilst 5 indicates the difference in SWB in theyear after and before the accident. The parameter 6 measures the change (after/before) in
SWB in the tsunami regions relative to unaffected regions. Since those regions differ withrespect to their location relative to the (unaffected) reference region, 6 is expected to be
negative.
Measuring distance we mapped in a first step the location of existing nuclear power stations
using GIS (see Figure 1 above).12 Next, we measured the distance of each existing nuclear
power station to each municipality.13 Distance to the Fukushima Dai-ichi power station varies
between 57 and 1,771 km, being 487 km on average. 14
To estimate the impact of the accident on SWB based on distance measures we use thefollowing equation:

β

β

β

∗

3

where the key variable of interest, Dj, represents the distance to the Fukushima Dai-ichi power
plant (Ei = Dj). The coefficient of the interaction term of the post-Fukushima dummy with
distance to the Fukushima power plant,6, is expected to be positive.
An alternative functional form to capture proximity to the Fukushima site is given by:

β

β

β

β

11

1

∗

4

The value is one if dead or injured persons were reported or if destroyed or damaged buildings were reported
for the respective municipality.
12
Information on location of existing nuclear power plants was obtained from the National Land Numerical
Information Download Service (http://nlftp.mlit.go.jp/ksj-e/gml/gml_datalist.html, last accessed, 29-04-2013).
13
We used the centroid of each municipality.
14
In both of these specifications we continue to include dummy variables for the prefectures.

9This functional form presumes that the effect declines with distance in a hyperbolic fashion(Ei = 1/Dj), whereas the previous formulation involves a linear decay with distance. In thisformulation 6 is expected to be negative.
In addition to affecting SWB through its physical consequences, the Fukushima accident may
have led to increased worry about nuclear power in general, which may have affected the
SWB especially of people living close to nuclear power plants. The corresponding
specifications directly follow equations (3) and (4) now measuring distance to the closest
existing nuclear power plant instead of Fukushima. Another functional form for the SWB
function is the use of count variables to identify the number of nuclear power stations, Z,
located in a specific concentric circle with the municipality at the centre e.g. 0-25 km; 0-50km etc. (Ei = Zj).
In our sample the minimum distance to the closest nuclear power station varies between 18
and 675 km with an average of 124 km. Within a radius of 100 km 68% of the households
have at least one nuclear power station, some have up to four.
In estimating these models, SWB will be measured by “happiness with one’s life in theprevious year”, which in 2012 refers to the time after the disaster. However, it may be thecase that consequences of the tsunami and the nuclear accident influenced people’s
assessment not only of their well-being in the year following the event but also of their entire
life. To test this hypothesis, we re-estimated some of the above models with “happiness with
one’s whole life” as the dependent variable.

3.2 Data
Most of the data used to investigate the well-being effect of the disaster is taken from the
KHPS. The KHPS is a representative Japanese household panel conducted by Keio University
based on a set of pre-tested questionnaires for both households and individuals. The first waveof KHPS was assembled in 2004 and covered 4,005 households; the most recent one in 2012.
The usual sample size ranges between 3,000 and 3,500 households.15 Interviews are always
carried out in January.
KHPS provides information on various aspects of the participating individual and therespective households. The questionnaires comprehensively cover information on household
15

For aspects on representativeness of the data see Kimura (2005). For sample attrition in KHPS see Miyauchi etal. (2006), McKenzie et al. (2007), and Naoi (2008).

10composition, income, occupation, employment history, school attendance, lifestyle andlocation. In addition to a stable set of core questions, each year the survey focuses on special
topics. In 2011 and 2012 the questionnaire included for the first time questions on different
aspects of SWB. In order to take advantage of this information and because we are interested
in the well-being effects of the accident in March 2011, the analysis relies exclusively on thesurveys of 2011 and 2012 (fielded in January of the respective years). Taken together ourdataset contains a total number of 5,979 observations. Table A1 in the Appendix provides
information on the variables included and the summary statistics.
SWB is measured on an integer scale of 0–10 with an average SWB of 6.24 for our sample
when individuals are asked about their happiness referring to the previous year. 16 SWB
understood as happiness with one’s whole life up to the present, the average of this variable is6.46.17 Comparing the two SWB measures over time, average SWB decreased from 2011 to2012 (from 6.25 to 6.23) for the first measure but increased for the second (from 6.45 to 6.47).
Explanatory variables, generally found significant in models explaining differences in SWB,
are individual characteristics such as age, gender, employment status, education level, andincome, number of children, physical condition or marital status. Income enters the regression
equation in its natural logarithm to account for declining marginal utility of income.
Controls at the municipality level include population density, the elevation of the municipality
as well as latitude and longitude to control for differences in climate.18 At the prefecture level
we include information on the rate of unemployment.19 Note that in a macroeconomic sense,
the effect of the March 2011 disasters on employment is generally minor.20 To capture further
regional differences dummy variables indicate in which of the 47 prefectures the individual isliving. A further dummy variable indicates whether observations are drawn from the 2011 or2012 survey.

16

The exact wording of the question is: Please choose a number on a scale of 0 to 10, where “0” means having
no feeling of happiness at all and “10” means fully having a feeling of happiness over the last one year
17
The corresponding question in the KHPS is: Please choose a number on a scale of 0 to 10, for which “0”means having no feeling of happiness at all and “10” means fully having a feeling of happiness for the whole life
up to the present.
18
Information on population density was taken from the Population Census. Information on elevation wasprovided by the National Land Numerical Information Download Service (http://nlftp.mlit.go.jp/ksje/gml/gml_datalist.html, last accessed, 29-04-2013). Elevation, latitude and longitude refer to the centroid of amunicipality. There are 1,719 municipalities in total as of January 2013. For 2011 and 2012 KHPS covers 455
municipalities. In our sample the average municipality has a size of about 200 square kilometer.
19
Data was taken from the Labor Force Survey (Ministry of Internal Affairs and Communications of Japan).
20
The number of job seekers in the three most affected prefectures (Fukushima, Miyagi, Iwate: the total
population is about 5.7 million) peaked at about 164,000 in June 2011 but declined afterwards (Ministry of
Health, Labor and Welfare, http://www.mhlw.go.jp/stf/houdou/2r9852000001z9f4.html, last accessed on May 24,2013).,

11

4. Results
Based on the results by Ferrer-i-Carbonell and Frijters (2004) our empirical analysis uses OLS.
In order to account for the possible correlation of residuals when observations are taken fromthe same individual over time, we report robust standard errors. The effect is to increase thestandard errors of the parameter coefficients. This procedure also leads to robust varianceestimates in the face of heteroskedasticity.

4.1 Basic Results
Table 1 presents the results of the first two model specifications using SWB as referring to theprevious year as the dependent variable. Model 1 is a truncated version of equation (1) whichomits the event variables, variables Ei and Ei * Yi..

Table 1 about here

Results of Model 1 confirm those of earlier studies using data from other countries (see, e.g.,
Dolan et al. 2008). Consistent with earlier analyses we find a U-shaped relationship with age21
and a positive and diminishing marginal utility of income. Unemployed, sick and single
individuals are significantly less happy than their counterparts. Individuals with higher levelsof education or those currently being in education are happier as well as those being inretirement or those doing housekeeping or taking care of the kids. The presence of children
has no significant effect on SWB but the size of the household has. It is negative. Being amale effects SWB negatively. Turning to the geographical variables; people living in
Kanagawa, a highly populated prefecture close to Tokyo, seem to be happier than those in thereference region (Iwate, Miyagi and Fukushima). The coefficient is significant only at the 10
percent level of confidence (results for prefectures are not presented individually). All othervariables are statistically not significant. Comparing observations from the 2011 survey to the2012 survey reveals no significant difference.
The results of Model 2 (equation 2), also presented in Table 1, indicate that before theaccident SWB in the areas affected by the tsunami was not significantly different from those
21

Happiness is lowest at the age of 45 years.

12

in other areas. The interaction effect, however, is negative, as expected and statisticallysignificant. Post-Fukushima location mattered: individuals located in areas not directly
affected by the tsunami experienced higher SWB. The F-statistic of joint significance of T and
Y*T, however, results in a P-value of 0.1003 only. The other results are comparable to thoseof Model 1.
Turning to the results of the Models capturing distance to the Fukushima site, results ofModel 3 (equation 3) suggest that before the accident distance to the Fukushima site had no
statistically significant effect on people’s SWB. The interaction effect, however, is positive,
as expected, and statistically significant. Post-Fukushima the distance to the power plantmattered: individuals experienced higher SWB the more distant from the place of the accident
they lived. Looking at results for Model 4, the inverse distance specification (equation 4), we
find a statistically significant effect on SWB before and after the accident. The estimatedcoefficient is positive before the accident and, as expected, negative after the accident. The
other results are comparable to those of Model 1. The positive coefficient could be areflection of strong economic standings of the areas around the Fukushima site before theaccident, which seems to be better captured by the hyperbolic than by the linear
specification.22

4.2 Valuing the treatment effectTo investigate the treatment effect (parameter 6 in equations (1) to (4)), the DD-estimatorcan be written as (Meyer 1995):

,

,

,

,

5

The parameter 6 would be statistically not different from zero, if we assume that in theabsence of the disaster SWB would have changed identically in the treatment group T and thecontrol group C. Our results suggest otherwise
22

In Japan, surrounding areas of nuclear power plants enjoy various economic benefits, such as the subsidies
based on the Power Source Siting Laws (dengen san pou) and large property tax revenues from the siting powercompany. Also, the power company and related businesses tend to constitute a dominant proportion of the local
economy around a nuclear power plant. For example, before the Fukushima accident, more than 60% of theeconomic output in the Futaba county (where the Fukushima Dai-ichi power plant is located) was electricity
related (source: the Reconstruction Agency website,
http://www.reconstruction.go.jp/topics/20120904_sangyoukoyouplan.pdf, last accessed, 29-04-2013).

13

We now use our results to compute the amount a person would be willing to pay to avoid
being treated. To this purpose we convert the treatment effect in equation (5), which ismeasured in units of SWB, into monetary units by dividing it by/, where M

denotes income (with I = lnM).23
Results of Model 2 suggest that the monetary equivalent for a household with average income
is about ¥ 3,700,000 to avoid living in a municipality affected by the tsunami. This
corresponds to 72 percent of annual average income. Post-Fukushima the treatment effect fora one kilometer increase in distance from the Fukushima site is ¥ 6,800 for the average
household (Model 3).24 Applying results of Model 4 numbers are about ¥ 5,000 for mean
distance (487 km); about ¥ 340,000 for minimum (57 km) and about ¥ 350 for maximum(1771 km) distance. The figure of ¥ 340,000 for a move from 57 km (minimum distance) to
58 km from the Fukushima site corresponds to 6.6 percent of average income. For a nonmarginal change in distance, e.g. from minimum distance to a distance of 100 km, thecompensating surplus for the same household is about ¥ 1,015,000 or 20 percent of income.
The results are comparable, although at the higher end, to those of others investigating othersingle events. Lüchinger and Raschky (2009), for example, calculate an average WTP of one
sure flood event in the region of residence corresponding to 24 percent of an average
household income. Caroll et al. (2009) calculated that a drought in Australia is equivalent toan income loss of 38 percent for a household with mean household income.

4.3 Extensions
The first extension relates to the hypothesis that, in addition to being affected by the physicalconsequences of the disaster, well-being is influenced by increased worry about nuclear
power among those living close to nuclear power plants or by the number of nuclear power
stations in the vicinity. According to Models 5 to 10 (Table 2), distance to or presence of
nuclear power stations in general is mostly insignificant. An exception is Model 9 where thenumber of nuclear power stations within a distance of 75 km has a significant effect on SWB,
but the coefficient is significant only at the 10 percent level and the F-statistic of joint
significance of Y and Z*Y results in a P-value of 0.1633.

23
24

Formally, this way we compute the marginal rate of substitution of income for treatment.
The average net household income in our sample is ¥ 5,100,000 (evaluated with the average exchange rate ofJanuary 2012, ¥ 100 is worth US$ 1.30).

14

Table 2 about here

The second extension relates to our dependent variable, which up to this point was people’s
happiness in the year before the respective interview. In the following we analyze if theaccident has an effect on SWB understood as happiness with one’s whole life up to thepresent. Table 3 presents results of specifications equivalent to Models 1 to 4, where thereplacement of the dependent variable is the only difference. Comparing the results to thosepresented in Table 1 we find no change in sign and significance of most variables previously
included. An important exception is the pre-Fukushima effect. In all three models, Models 2a
to 4a, we find no statistically significant effect for the respective variables.25 In Model 4a theF-statistic on joint significance of Y and (1/D)*Y results in a P-value of 0.0181. Another
difference is the insignificant effect of unemployment. Though the accident seems to haveaffected people’s evaluation of the quality of their lives after the event, it has not influenced
their assessment of their life as a whole.

Table 3 about here

4.4 Robustness Checks
Finally, we test whether our results are robust to alternative specifications (results are notpresented). Replacing the distance measures by a variable measuring the distribution ofradioactive substances across municipalities, we analyze the effect of air dose rate on SWB.
By doing so, we account for the fact that the prevailing wind direction after the accident
affected regions close to Fukushima very differently. A distance measure would not be able topick this up. The new variables are not individually nor jointly significant at the 10% level ofconfidence (F (2,5902)=0.25, P=0.7824).26
Further, when replacing the tsunami dummy, T in equation (2), by a dummy with value one if
dead persons were reported in the respective municipality, the results are comparable to those25

This is true for the other model specifications as well which are not presented here.
Information is taken from the Database on the Research of Radioactive Substances Distribution provided by
the Ministry of Education, Culture, Sports, Science and Technology (http://radb.jaea.go.jp/mapdb/en/). The
average of total cesium-134 and -137 depositions per municipality on April 29, 2011 was used. This is theearliest day after the accident for which data of wide-scale airborne measurements are available.
26

15

of Model 2 with a slightly higher 6-coefficient (-0.4101). When replacing the tsunami
dummy with a dummy indicating if buildings were completely destroyed in the respective
municipality, the coefficient 6 is smaller (-0.0001). In both cases, the new variables areindividually and jointly significant. So far, we have not considered the possibility of selection
bias, that is, people most seriously suffering from the event refused to participate in the 2012
round of the survey. In addition, given that all residents within a certain radius from the
Fukushima Dai-ichi power plant were evacuated to more distant places after the event, it islikely that the location of some of the interviewees as of 2012 was more distant than it was at
the time of the disaster. This relocation effect may introduce a downward bias into therelationship between distance and the change in SWB. Both the selection and the relocation
effect, if present, will lead to a downward bias in our estimates of the well-being effect of the
Fukushima disaster. Further, people that lived in the affected areas or closer to Fukushima
before the disaster are perhaps more likely to get unemployed or to have health related
problems.
When omitting the variables measuring income, health and presence of unemployment from
Model 4, results of the remaining variables are robust. The variables measuring the inverse
distance to the Fukushima site (Prox_Fukushima) are still highly significant, the coefficient of
the interaction term 2012*Prox_Fukushima gets a bit smaller (-73.66 compared to -80.41 inModel 4). 27 Similar results are obtained when restricting the sample to those individuals thatparticipated in both years in the KEIO panel. Restricting the sample further by excluding
those that moved does not change the results. 28 Next, including a term interacting
unemployment and inverse distance to the Fukushima site to test if unemployed persons
closer to Fukushima show lower levels of SWB, the term has the expected negative sign andis significant at the 5% level.29 Finally, including a term interacting health status and inverse
distance to the Fukushima site we test if individuals that report lower levels of health status
are unhappier if they live closer to the Fukushima site. The term is positive, as expected, andsignificant at the 5% level confirming the hypothesis. 30 Other results are unchanged.

27

F(2, 5907)=7.18, P=0.0008.
In general, few individuals moved between the two survey years (only 30) and no regional pattern isobservable. The drop-out rate between the 2011 and 2012 samples shows no bias towards locations that weremore heavily affected. It reduces our sample to 5,008 observations. Also, the 2011 KEIO samples include nohouseholds located in the evacuation zone.
29
F-Text on joint significance of Unemployed + Unemployed*Prox_Fukushima results in F(2,4860)=5.20,
P=0.0056.
30
We tried both; health measured as categorical variables as in Model 5 and as a continuous variable. The F-test
for the specification with the continuous variable (Health + Health* Prox_Fukushima) resulted in F(2,4863)=229.94, P=0.0000.
28

16

Combined, irrespective of the robustness checks we perform the interaction term of postFukushima dummy and inverse distance is negative and highly significant.

5. Discussion and Conclusion
Using data from KHSP, we conducted for the first time a quasi-experimental SWB study ofthe combined disaster of an earthquake, tsunami and a nuclear accident near Fukushima on
March 11, 2011 taking into account the spatial dimension.
With regard to the factors of SWB we found evidence highly consistent with findings from
Western countries in spite of differences in the cultural background. 31 The plausibility of
those results enhances our confidence in the suitability of the KHSP database for studying themain issue of this paper, the effects of the Fukushima events on SWB. Possible SWB effectsof Fukushima relate, in the first place, to the physical consequences of the disaster (inparticular fatalities to relatives and friends, destruction of buildings and infrastructures,
release of radiation, evacuation of residents, shortage of basic necessities such as electricity
and water). In the second place, it is conceivable that people’s well-being dropped even if notdirectly affected due to increased worry about nuclear power. Finally, for those most seriously
affected by the disaster it could be that not only the assessment of their happiness after the
event deteriorated, but also their assessment of their entire life.
One main finding from our empirical analysis is a significant drop in SWB after the disasterin places affected by the tsunami but not elsewhere. Another one is a significant drop in SWB
proportional to proximity to the place of the nuclear accident, the Fukushima Dai-ichi nuclear
power plant. This result is robust to a number of robustness checks. In particular, it is notaffected by selection bias or relocation bias. Given that the physical effects of the nuclear
accident decay with distance, we take this as evidence of an effect of the nuclear disaster on
SWB. In our preferred specification we assume that the effect size decreases with distance ina hyperbolic fashion. Based on this model, we find the monetary equivalent of a 1-km
increase in distance to the Fukushima site to be about ¥ 6,800 for a household with average
income at mean distance. For a household at minimum distance to the Fukushima nuclear
plant, which is 57 km, the monetary equivalent of a 1-km increase in distance is about¥ 340,000 or 6.6 percent of average annual income. The monetary equivalent goes to up to 72
31

Culture-specific and unique characteristics to Japan or East Asia, originating from either prevailing
philosophical traditions (e.g., Buddhism) or traits of collectivism are still present in Japan despite ongoing shifts
towards individualism due to globalization. For a review see Tov and Diener (2007) and Uchida and Ogihara(2012).

17percent of annual average income for those living in affected regions. These numbers arecomparable at the higher end to those of others analyzing other single events, like floods anddroughts.
In interpreting these results it should be noted that the underlying regressions control forincome, the employment status, and the health status. Therefore, the above figures represent
the intangible effects in the strictest sense, that is, disregarding effects from lost jobs, reduced
income, and sickness, all of which contribute to SWB in a statistically and substantively
significant way.
In contrast to the physically-based effects on SWB we find no evidence of a psychologicaleffect in terms of increased worry about nuclear power for those living close to nuclear power
plants. However, this lack of an effect is plausible because all the nuclear power plants in the
country were suspended after the Fukushima disaster. Therefore, this finding lends additional
credibility to our approach and findings rather than discrediting them.32
Finally, in spite of the significant and sizeable effects on SWB after the disasters, people’s
assessments of the quality of their entire life do not seem to have been affected by them. Thismight be explained by the philosophical traditions influential in East Asia (e.g., Buddhism
and Daoism) emphasizing the dialectic nature of things, and East Asians in fact haverelatively accepting attitudes towards negative emotions and events. This also relates to how
East Asians regard happiness. For example, Uchida and Kitayama (2009) find that theJapanese participants in their experiment tend to associate happiness even with some negative
features, such as jealousy from others and inattention to one’s surroundings, and that thisresult is in contrast to that of the American participants of the experiment, whose view ofhappiness tends to be entirely positive.
As to the limitations of this study, we are able to measure short-run effects only. This may
explain why we found no effects of the level of radiation, as these effects may be of a more
long-term character.

Acknowledgement
We would like to thank Swantje Sundt and Tim Hartmann for outstanding research assistanceand Susana Ferreira and Arik Levinson for insightful comments. The data analysis in this32

As of May 2013, 48 out of 50 commercial nuclear reactors in Japan are still suspended or are to be
decommissioned.

18

paper utilizes Keio Household Panel Survey (KHPS) data provided by the Panel Data
Research Center at Keio University. The Excellence Initiative Future Ocean provided
welcome financial support. The usual disclaimer applies.

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22

Table 1: Regression results

Individual Information
IncomeAge
Age (squared)
Male
Size
SingleMarried
Other
ChildrenEdulevel 1
Edulevel 2
Edulevel 3
Edulevel 4
Edulevel 5
Edulevel 6
Edulevel 7
Employed
Student
Retired
UnemployedHome
Health 1
Health 2
Health 3
Health 4

Model 1Model 2Model 3Model 4

Coefficient Standard Error

Coefficient Standard Error

Coefficient Standard Error

Coefficient Standard Error

0.3699 0.0477 ***-0.0701 0.0176 ***0.0008 0.0002 ***-0.2994 0.0621 ***-0.1257 0.0279 ***(reference category)
0.8526 0.0879 ***0.0035 0.2028
0.0531 0.0378(reference category)
0.1992 0.1206 *0.2674 0.1438 *
0.3976 0.1324 ***-0.0286 0.2746
0.4634 0.1355 ***-0.2257 0.2329(reference category)
0.8526 0.3268 ***0.2227 0.1167 **-0.6305 0.2241 ***0.1910 0.0812 **(reference category)-0.6767 0.0918 ***-1.4563 0.0897 ***-2.2974 0.1155 ***0.37070.0476 ***-0.07100.0176 ***0.00080.0002 ***-0.2938
0.0620 ***-0.12450.0279 ***(reference category)
0.85080.0879 ***0.00350.20190.05310.0378(reference category)
0.19880.1206 *0.26630.1437 *0.39110.1324 ***-0.03960.2752
0.46110.1356 ***-0.23410.2319(reference category)
0.85180.3273 ***0.21090.1163 *-0.62300.2251 ***0.19450.0810 **(reference category)-0.67200.0918 ***-1.45290.0898 ***-2.29140.1155 ***0.37250.0476 ***-0.07120.0176 ***0.00080.0002 ***-0.2969
0.0620 ***-0.12420.0279 ***(reference category)
0.85040.0879 ***0.00140.2012
0.0536
0.0378(reference category)
0.19550.12070.26100.1436 *0.38760.1324 ***-0.04170.27470.45900.1355 ***-0.23730.2323(reference category)
0.84900.3280 ***0.20950.1165 *-0.61610.2257 ***0.18920.0811 **(reference category)-0.67480.0917 ***-1.4572
0.0898 ***-2.2972
0.1154 ***0.37240.0476 ***-0.0700
0.0176 ***0.00080.0002 ***-0.2923
0.0620 ***-0.12290.0279 ***(reference category)
0.85230.0878 ***0.00750.20120.05210.0378(reference category)
0.19500.11990.26520.1431 *0.38970.1318 ***-0.0367
0.27430.46380.1350 ***-0.23390.2326(reference category)
0.85270.3281 ***0.20330.1162 *-0.61440.2253 ***0.19050.0811 **(reference category)-0.67210.0917 ***-1.45390.0896 ***-2.2944
0.1151 ***23

Health 5
Geographical and other information
Tsunami2012*Tsunami
Fukushima
2012*Fukushima
FukushimaInv
2012*FukushimaInv
Unemployment
Popdens
Elevation
Longitude
Latitude2012
Constant
RegionR2
F-Test (P>F)
N-3.42120.3520 ***-0.1104 0.1966
0.0000 0.0000
0.0000 0.0002-0.0803 0.1034
0.0769 0.1132
11.3031 13.0449-0.1104 0.1966
YES
0.18045979-3.40520.3501 ***0.1123-0.26820.12920.1267 **-0.1068
1.40E-06-0.0001-0.09300.25100.01589.34300.22379.30E-06
0.00020.10360.1262 **0.0822
12.2892

YES
0.18210.1008*5979-3.40630.3509 ***-0.0008
0.00050.00100.0002 ***-0.0015
0.21893.54E-07 9.39E-06-0.0001
0.0002-0.0952
0.10410.27360.1316 **-0.26760.1156 **9.4249 12.4856
YES
0.18270.0155**5979-3.3846

0.3513 ***178.2727-80.4063-0.1857-3.85E-07-0.0001-0.12780.27060.176913.590358.2195 ***23.1874 ***0.2242
9.32E-06
0.00020.10460.1263 **0.0995 *12.3956

YES
0.18390.0002***5979

Note: Significance at the ten-percent level is indicated by *, significance at the five-percent level is indicated by ** and significance at the one-percent level is indicated by ***.
a
Reported are robust standard errors.
b
F-Test on joint significance of Tsunami + 2012*Tsunami, Dist_Fukushima + 2012*Dist_Fukushima and Prox_Fukushima + 2012*Prox_Fukushima, respectively.

24

Table 2: Alternative specifications

Model 5
Coefficient
Individual Information
Income0.3707 ***Age-0.0709 ***Age (squared)
0.0008 ***Male-0.2951 ***Size-0.1236 ***SingleMarried0.8525 ***Other-0.0037
Children
0.0532
Edulevel 1
Edulevel 2
0.1897
Edulevel 3
0.2570 *Edulevel 4
0.3797 ***Edulevel 5-0.0410
Edulevel 6
0.4530 ***Edulevel 7-0.2360
Employed
Student
0.8546 ***Retired0.2146 *Unemployed-0.6143 ***Home
0.1931 **Health 1
Health 2-0.6719 ***Model 6
Coefficient

Model 7
Coefficient

0.3687 ***-0.0705 ***0.0008 ***-0.2932 ***-0.1237 ***0.3686 ***0.3690 ***-0.0708 ***-0.0716 ***0.0008 ***0.0008 ***-0.2925 ***-0.2945 ***-0.1237 ***-0.1239 ***(reference category)
0.8488 ***0.8492 ***-0.0008
0.0021
0.0532
0.0532(reference category)
0.19860.2006 *0.2671 *0.2685 *0.3923 ***0.3960 ***-0.0320-0.0399
0.4611 ***0.4634 ***-0.2318-0.2236(reference category)
0.8561 ***0.8551 ***0.2153 *0.2130 *-0.6228 ***-0.6226 ***0.1956 **0.1931 **(reference category)-0.6723 ***-0.6752 ***0.8500 ***-0.0039
0.05300.19520.2630 *0.3875 ***-0.03180.4584 ***-0.23530.8579 ***0.2152 *-0.6190 ***0.1942 **-0.6699 ***Model 8
Coefficient

25

Model 9Coefficient

Model 10
Coefficient

0.3704 ***-0.0702 ***0.0008 ***-0.2949 ***-0.1235 ***0.3696 ***-0.0713 ***0.0008 ***-0.2951 ***-0.1246 ***0.8528 ***-0.00440.05180.8495 ***-0.0023
0.05390.19020.2531 *0.3784 ***-0.0349
0.4528 ***-0.2532

0.1966
0.2636 *0.3866 ***-0.03270.4589 ***-0.23840.8647 ***0.2159 *-0.6140 ***0.1922 **0.8599 ***0.2138 *-0.6162 ***0.1922 **-0.6712 ***-0.6747 ***Health 3-1.4539 ***Health 4-2.2904 ***Health 5-3.4010 ***geographical and other information
Nuclear-0.00242012*Nuclear
0.0002
NuclearInv
2012*NuclearInv
Nuclear 25
2012*Nuclear25
Nuclear 50
2012*Nuclear50
Nuclear 75
2012*Nuclear75
Nuclear 100
2012*Nuclear100
Unemployment-0.0114
Popdens
1.90E-06
Elevation-0.0001
Longitude
0.0174
Latitude0.2453 *2012-0.0554
Constant-3.7719
RegionYES
R20.1818
F-Test (P>)
0.3121N
5,979-1.4517 ***-2.2885 ***-3.3937 ***-1.4534 ***-2.2911 ***-3.3986 ***-1.4570 ***-2.2949 ***-3.4100 ***-1.4536 ***-2.2876 ***-3.3950 ***-1.4563 ***-2.2930 ***-3.3997 ***8.2069-1.8816
0.2618-0.1170-0.14100.00550.1435 *-0.0085-0.0012
1.36E-06-0.0001-0.06450.2481 *-0.00615.0483YES
0.18160.5472
5,979

0.0041
1.43E-06-0.0001-0.08490.2494 *-0.0230
7.6133YES
0.18160.7224
5,979-0.0004
7.53E-07-0.0001-0.1058
0.2631 *-0.02639.9877
YES
0.18170.5087
5,979

0.00201.02E-06-0.0001-0.06780.2519 *-0.0218
5.3442YES
0.18190.1633
5,979

0.01520.0529-0.0071
6.55E-08-0.0001-0.08640.2505 *-0.0634
7.8924YES
0.18170.38895,979

Note: Significance at the ten-percent level is indicated by *, significance at the five-percent level is indicated by ** and significance at the one-percent level is indicated by ***.
a
F-Test on joint significance of distance variables.

26

Table 3: Alternative dependent variable
Model 1a
Coefficient

Model 2aCoefficient

Model 3aCoefficient

Model 4aCoefficient

Individual Information
Income0.3868 ***Age-0.0563 ***Age (squared)
0.0006 ***Male-0.2510 ***Size-0.0978 ***SingleMarried0.7418 ***Other
0.1108Children
0.0662 *Edulevel 1
Edulevel 2
0.2699 **Edulevel 3
0.4643 ***Edulevel 4
0.6704 ***Edulevel 5
0.5581 **Edulevel 6
0.6366 ***Edulevel 7-0.0753
Employed
Student
0.8922 ***Retired0.1413
Unemployed-0.2368Home
0.1469 **Health 1
Health 2-0.4748 ***Health 3-1.1340 ***Health 4-1.6156 ***Health 5-2.4292 ***Geographical and other information
Tsunami2012*Tsunami
Fukushima
2012*Fukushima
FukushimaInv
2012*FukushimaInv
Unemployment-0.0694
Popdens
1.11E-05
Elevation
4.54E-05
Longitude-0.0806
Latitude0.1977 *2012-0.0205
Constant
6.0541-0.1023
1.13E-05
4.34E-05-0.07900.1957 *-0.0082
7.9859-0.0701
1.10E-05
4.41E-05-0.08070.2004 *-0.05898.0456144.0957 ***-12.0343-0.1070
9.48E-06
5.82E-05-0.11470.2205 *0.0077
11.7842

RegionR2

YES
0.1651YES
0.1650YES
0.1661YES
0.16500.3876 ***0.3873 ***-0.0563 ***-0.0563 ***0.0006 ***0.0006 ***-0.2510 ***-0.2514 ***-0.0980 ***-0.0978 ***(reference category)
0.7423 ***0.7419 ***0.11310.11100.0660 *0.0663 *(reference category)
0.2691 **0.2693 **0.4628 ***0.4633 ***0.6687 ***0.6695 ***0.5550 **0.5568 ***0.6349 ***0.6360 ***-0.0753-0.0762(reference category)
0.8878 ***0.8911 ***0.14070.1408-0.2384-0.23630.1472 **0.1463 **(reference category)-0.4748 ***-0.4753 ***-1.1334 ***-1.1344 ***-1.6150 ***-1.6166 ***-2.4279 ***-2.4300 ***0.3884 ***-0.0549 ***0.0006 ***-0.2481 ***-0.0964 ***0.7440 ***0.11500.0651 *0.2649 **0.4619 ***0.6667 ***0.5533 **0.6368 ***-0.07510.8904 ***0.1356-0.23170.1454 **-0.4727 ***-1.1319 ***-1.6160 ***-2.4146 ***0.0015-0.0802-0.0001
0.000127

F-Test (P>F)

N

5979

0.72140.88100.0181 **

5979

5979

5979

Note: Significance at the ten-percent level is indicated by *, significance at the five-percent level is indicated by** and significance at the one-percent level is indicated by ***.
a
F-Test on joint significance of distance variables.

28

Figure 1: Distribution and location of nuclear power plants in Japan

Source: Own presentation.

29

Appendix Table A1: Definition of variables and summary statistics
Variable

Definition

Mean

Std. Dev.

Min

Max

Individual information
Happy (lastyear)
Happy (wholelife)
Income6.24292.24870

106.45932.02340

10

15.25560.6546

11.16

18.04

52.1500

13.268320780.49020.4999013.28381.44891

10Unity of individual is a single, zerootherwise
Unity if individual is married, zerootherwise
Unity if individual is divorced or married,
zero otherwise
number of children living in household

0.19800.3985010.7764

0.41670

10.02560.1579011.1387

1.12230

7Unity if individual has a jr. high school
degree, zero otherwise
Edulevel 2
Unity if individual has a high school
degree, zero otherwise
Edulevel 3
Unity if individual has a jr. college ortechnical college degree, zero otherwise
Edulevel 4
Unity if individual has an university
degree, zero otherwise
Edulevel 5
Unity if individual has a graduate school
degree, zero otherwise
Edulevel 6
Unity if individual has another degree,
zero otherwise
Edulevel 7
Unity if individual has no degree, zerootherwise
Employed
Unity if individual is employed (including
self-employment), zero otherwise
Student
Unity if individual is a in education, zerootherwise
Retired
Unity if individual is retired, zerootherwise
Unemployed
Unity if individual is unemployed, zerootherwiseHome
Unity if individual ishousekeeping/childraising, zero otherwise
Health 1
Unity if individual has a good healthstatus, zero otherwise
Health 2
Unity if individual has a pretty good
health status, zero otherwise
Health 3
Unity if individual has a normal healthstatus, zero otherwise
Health 4
Unity if individual has a not so good
health status, zero otherwise
Health 5
Unity if individual has a bad health status,
zero otherwise
geographical information

0.06740.2507010.40730.49140

10.11930.32410

10.18450.3879010.01340.1149010.18000.38420

10.02020.1408

0

1

0

1

Age
Male
Size
SingleMarried
Other
Children

Average score of self-reported happinesson a 0-10 scale
Average score of self-reported happinesson a 0-10 scale
Logarithm of annual household net
income (in 10,000 ¥)Age of the individual
Unity if individual is a male, zerootherwise
Size of household

Edulevel 1300.74610.00800.08920

10.06410.2449010.01870.1356010.16310.3695010.12530.33110

10.28930.4535010.43850.4962010.1351

0.3419010.01170.107601Unemployment

Rate of unemployment (%)

4.62830.76782.2

7.6

Popdens

4198.6820

4927.4170

5.921881.5

Elevation

Population density in persons per square
kilometerin m

106.7725

177.811001374

Longitudein °137.0974

3.3778127.69144.28Latitudein °35.5988

2.3850

26.15

44.31

Tsunami0.25870.438001

124.1223

75.037918675

0.01070.00690.00150.0564

Dist_Fukushima

Unity if individual is living in a
municipality affected by the tsunami, zerootherwise
Minimum distance to next operating
nucelar power plant (km)Inverse minimum distance to next
operating nucelar power plant (km)Distance to Fukushima I (km)487.1440

308.9464

57

1771

Prox_Fukushima

Inverse Distance to Fukushima I (km-1)

0.00310.00230.00060.0174

Nuclear 25

25 km radius (Number of nuclear power
plants in a radius of 25 km)
50 km radius (Number of nuclear power
plants in a radius of 50 km)
75 km radius (Number of nuclear power
plants in a radius of 75 km)
100 km radius (Number of nuclear power
plants in a radius of 100 km)
150 km radius (Number of nuclear power
plants in a radius of 150 km)
Unity if observations are drawn from the2012 survey, zero otherwise

0.01440.11910

10.07540.2997040.30490.62260

40.68020.9830041.7516

1.66110

50.55430.49710

1Nuclear
NuclearInv

Nuclear 50
Nuclear 75
Nuclear 100
Nuclear 150
2012

Source: Own presentation31